{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T13:21:12Z","timestamp":1776950472289,"version":"3.51.4"},"reference-count":119,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016249","name":"Albaha University","doi-asserted-by":"publisher","award":["This study is part of a PhD program which is funded by Al Baha University"],"award-info":[{"award-number":["This study is part of a PhD program which is funded by Al Baha University"]}],"id":[{"id":"10.13039\/100016249","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (<jats:italic>\u03c7<\/jats:italic><jats:sup>2<\/jats:sup> = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow\u2019s milk (OR = 2.92, P = 0.000), birth weight &gt;4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age &gt;25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.<\/jats:p>","DOI":"10.1371\/journal.pone.0282426","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T18:27:55Z","timestamp":1677695275000},"page":"e0282426","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3791-2340","authenticated-orcid":true,"given":"Ahood","family":"Alazwari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alice","family":"Johnstone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laleh","family":"Tafakori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mali","family":"Abdollahian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed M.","family":"AlEidan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid","family":"Alfuhigi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mazen M.","family":"Alghofialy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulhameed A.","family":"Albunyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hawra","family":"Al Abbad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam H.","family":"AlEssa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulaziz K. H.","family":"Alareefy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad A.","family":"Alshamrani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"issue":"1","key":"pone.0282426.ref001","doi-asserted-by":"crossref","first-page":"e3075","DOI":"10.1002\/dmrr.3075","article-title":"Incidence and trend of type 1 diabetes and the underlying environmental determinants","volume":"35","author":"Y Xia","year":"2019","journal-title":"Diabetes Metab Res Rev"},{"issue":"6","key":"pone.0282426.ref002","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1046\/j.1464-5491.1999.00076.x","article-title":"The British Diabetic Association Cohort Study, II: cause-specific mortality in patients with insulin-treated diabetes mellitus","volume":"16","author":"SP Laing","year":"1999","journal-title":"Diabetic 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